Overview

Dataset statistics

Number of variables29
Number of observations85821
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.2 MiB
Average record size in memory588.4 B

Variable types

Numeric19
Categorical9
DateTime1

Alerts

Q com has constant value "0.0" Constant
$ com has constant value "0.0" Constant
Q tra has constant value "0.0" Constant
subgrupo has a high cardinality: 2301 distinct values High cardinality
Ref. has a high cardinality: 2313 distinct values High cardinality
Descripcion has a high cardinality: 2242 distinct values High cardinality
df_index is highly correlated with AñoHigh correlation
Año is highly correlated with df_indexHigh correlation
q ini is highly correlated with $ ini and 8 other fieldsHigh correlation
$ ini is highly correlated with q ini and 8 other fieldsHigh correlation
Q pro is highly correlated with $proHigh correlation
$pro is highly correlated with Q proHigh correlation
Q ven is highly correlated with q ini and 8 other fieldsHigh correlation
$ ven is highly correlated with q ini and 8 other fieldsHigh correlation
Q dev is highly correlated with q ini and 8 other fieldsHigh correlation
$ dev is highly correlated with q ini and 8 other fieldsHigh correlation
Q s/e is highly correlated with $ s/eHigh correlation
$ s/e is highly correlated with Q s/eHigh correlation
q fin is highly correlated with q ini and 8 other fieldsHigh correlation
$ fin is highly correlated with q ini and 8 other fieldsHigh correlation
Q ven2 is highly correlated with q ini and 8 other fieldsHigh correlation
$ ven3 is highly correlated with q ini and 8 other fieldsHigh correlation
df_index is highly correlated with AñoHigh correlation
Año is highly correlated with df_indexHigh correlation
q ini is highly correlated with $ ini and 7 other fieldsHigh correlation
$ ini is highly correlated with q ini and 4 other fieldsHigh correlation
Q pro is highly correlated with q ini and 6 other fieldsHigh correlation
$pro is highly correlated with Q pro and 6 other fieldsHigh correlation
Q ven is highly correlated with q ini and 6 other fieldsHigh correlation
$ ven is highly correlated with q ini and 8 other fieldsHigh correlation
Q dev is highly correlated with $ devHigh correlation
$ dev is highly correlated with Q devHigh correlation
Q s/e is highly correlated with $ s/eHigh correlation
$ s/e is highly correlated with Q s/eHigh correlation
q fin is highly correlated with q ini and 8 other fieldsHigh correlation
$ fin is highly correlated with q ini and 5 other fieldsHigh correlation
Q ven2 is highly correlated with q ini and 6 other fieldsHigh correlation
$ ven3 is highly correlated with q ini and 8 other fieldsHigh correlation
df_index is highly correlated with AñoHigh correlation
Año is highly correlated with df_indexHigh correlation
q ini is highly correlated with $ ini and 6 other fieldsHigh correlation
$ ini is highly correlated with q ini and 6 other fieldsHigh correlation
Q pro is highly correlated with $proHigh correlation
$pro is highly correlated with Q proHigh correlation
Q ven is highly correlated with q ini and 8 other fieldsHigh correlation
$ ven is highly correlated with q ini and 8 other fieldsHigh correlation
Q dev is highly correlated with Q ven and 4 other fieldsHigh correlation
$ dev is highly correlated with Q ven and 4 other fieldsHigh correlation
Q s/e is highly correlated with $ s/eHigh correlation
$ s/e is highly correlated with Q s/eHigh correlation
q fin is highly correlated with q ini and 6 other fieldsHigh correlation
$ fin is highly correlated with q ini and 6 other fieldsHigh correlation
Q ven2 is highly correlated with q ini and 8 other fieldsHigh correlation
$ ven3 is highly correlated with q ini and 8 other fieldsHigh correlation
clase is highly correlated with $ com and 3 other fieldsHigh correlation
Año is highly correlated with $ com and 2 other fieldsHigh correlation
$ com is highly correlated with clase and 4 other fieldsHigh correlation
Q com is highly correlated with clase and 4 other fieldsHigh correlation
Q tra is highly correlated with clase and 4 other fieldsHigh correlation
Linea is highly correlated with clase and 3 other fieldsHigh correlation
df_index is highly correlated with Año and 3 other fieldsHigh correlation
Año is highly correlated with df_index and 1 other fieldsHigh correlation
pos is highly correlated with df_index and 2 other fieldsHigh correlation
Mes is highly correlated with df_index and 2 other fieldsHigh correlation
grupo is highly correlated with clase and 1 other fieldsHigh correlation
clase is highly correlated with grupo and 1 other fieldsHigh correlation
Linea is highly correlated with grupo and 1 other fieldsHigh correlation
q ini is highly correlated with $ ini and 8 other fieldsHigh correlation
$ ini is highly correlated with q ini and 5 other fieldsHigh correlation
Q pro is highly correlated with q ini and 9 other fieldsHigh correlation
$pro is highly correlated with q ini and 8 other fieldsHigh correlation
Q ven is highly correlated with q ini and 9 other fieldsHigh correlation
$ ven is highly correlated with q ini and 7 other fieldsHigh correlation
Q dev is highly correlated with Q pro and 7 other fieldsHigh correlation
$ dev is highly correlated with Q devHigh correlation
Q s/e is highly correlated with $ s/eHigh correlation
$ s/e is highly correlated with Q s/eHigh correlation
q fin is highly correlated with q ini and 9 other fieldsHigh correlation
$ fin is highly correlated with q ini and 6 other fieldsHigh correlation
Q ven2 is highly correlated with q ini and 9 other fieldsHigh correlation
$ ven3 is highly correlated with q ini and 7 other fieldsHigh correlation
fecha is highly correlated with df_index and 3 other fieldsHigh correlation
q ini is highly skewed (γ1 = 24.54171688) Skewed
Q pro is highly skewed (γ1 = 43.5957974) Skewed
$pro is highly skewed (γ1 = 27.24311778) Skewed
Q ven is highly skewed (γ1 = 45.70429977) Skewed
$ ven is highly skewed (γ1 = 27.38079609) Skewed
Q dev is highly skewed (γ1 = 79.99155308) Skewed
$ dev is highly skewed (γ1 = 59.99138643) Skewed
$ tra is highly skewed (γ1 = 78.88591598) Skewed
q fin is highly skewed (γ1 = 24.96065307) Skewed
Q ven2 is highly skewed (γ1 = 46.05782339) Skewed
$ ven3 is highly skewed (γ1 = 28.24911266) Skewed
df_index has unique values Unique
q ini has 1417 (1.7%) zeros Zeros
$ ini has 1417 (1.7%) zeros Zeros
Q pro has 79449 (92.6%) zeros Zeros
$pro has 79450 (92.6%) zeros Zeros
Q ven has 47591 (55.5%) zeros Zeros
$ ven has 47591 (55.5%) zeros Zeros
Q dev has 64985 (75.7%) zeros Zeros
$ dev has 64985 (75.7%) zeros Zeros
$ tra has 85567 (99.7%) zeros Zeros
Q s/e has 67469 (78.6%) zeros Zeros
$ s/e has 66839 (77.9%) zeros Zeros
Q ven2 has 48483 (56.5%) zeros Zeros
$ ven3 has 48455 (56.5%) zeros Zeros

Reproduction

Analysis started2022-07-02 04:34:32.684033
Analysis finished2022-07-02 04:37:00.054322
Duration2 minutes and 27.37 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct85821
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195931.3983
Minimum827
Maximum398103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:00.296950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum827
5-th percentile16347
Q195471
median192079
Q3295191
95-th percentile380377
Maximum398103
Range397276
Interquartile range (IQR)199720

Descriptive statistics

Standard deviation117205.2426
Coefficient of variation (CV)0.5981953052
Kurtosis-1.217445675
Mean195931.3983
Median Absolute Deviation (MAD)102799
Skewness0.0361842886
Sum1.681502854 × 1010
Variance1.37370689 × 1010
MonotonicityStrictly increasing
2022-07-01T23:37:00.648898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3958251
 
< 0.1%
1631731
 
< 0.1%
2632531
 
< 0.1%
239051
 
< 0.1%
2409911
 
< 0.1%
3579751
 
< 0.1%
1017231
 
< 0.1%
3099271
 
< 0.1%
2348401
 
< 0.1%
2553181
 
< 0.1%
Other values (85811)85811
> 99.9%
ValueCountFrequency (%)
8271
< 0.1%
8281
< 0.1%
8291
< 0.1%
8301
< 0.1%
8311
< 0.1%
8321
< 0.1%
8331
< 0.1%
8341
< 0.1%
8351
< 0.1%
8361
< 0.1%
ValueCountFrequency (%)
3981031
< 0.1%
3981021
< 0.1%
3981011
< 0.1%
3981001
< 0.1%
3980991
< 0.1%
3980981
< 0.1%
3980961
< 0.1%
3980941
< 0.1%
3980931
< 0.1%
3980921
< 0.1%

Año
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2020
36451 
2019
32608 
2021
16762 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters343284
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
202036451
42.5%
201932608
38.0%
202116762
19.5%

Length

2022-07-01T23:37:00.936799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T23:37:01.291046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
202036451
42.5%
201932608
38.0%
202116762
19.5%

Most occurring characters

ValueCountFrequency (%)
2139034
40.5%
0122272
35.6%
149370
 
14.4%
932608
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number343284
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2139034
40.5%
0122272
35.6%
149370
 
14.4%
932608
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Common343284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2139034
40.5%
0122272
35.6%
149370
 
14.4%
932608
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII343284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2139034
40.5%
0122272
35.6%
149370
 
14.4%
932608
 
9.5%

pos
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.934013819
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:01.566082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.479385174
Coefficient of variation (CV)0.5863459843
Kurtosis-1.188441114
Mean5.934013819
Median Absolute Deviation (MAD)3
Skewness0.2576769487
Sum509263
Variance12.10612119
MonotonicityNot monotonic
2022-07-01T23:37:01.747693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
38974
10.5%
58964
10.4%
48917
10.4%
28856
10.3%
18697
10.1%
126205
7.2%
116108
7.1%
106062
7.1%
95871
6.8%
75779
6.7%
Other values (2)11388
13.3%
ValueCountFrequency (%)
18697
10.1%
28856
10.3%
38974
10.5%
48917
10.4%
58964
10.4%
65610
6.5%
75779
6.7%
85778
6.7%
95871
6.8%
106062
7.1%
ValueCountFrequency (%)
126205
7.2%
116108
7.1%
106062
7.1%
95871
6.8%
85778
6.7%
75779
6.7%
65610
6.5%
58964
10.4%
48917
10.4%
38974
10.5%

Mes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.934013819
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:01.928869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.479385174
Coefficient of variation (CV)0.5863459843
Kurtosis-1.188441114
Mean5.934013819
Median Absolute Deviation (MAD)3
Skewness0.2576769487
Sum509263
Variance12.10612119
MonotonicityNot monotonic
2022-07-01T23:37:02.088855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
38974
10.5%
58964
10.4%
48917
10.4%
28856
10.3%
18697
10.1%
126205
7.2%
116108
7.1%
106062
7.1%
95871
6.8%
75779
6.7%
Other values (2)11388
13.3%
ValueCountFrequency (%)
18697
10.1%
28856
10.3%
38974
10.5%
48917
10.4%
58964
10.4%
65610
6.5%
75779
6.7%
85778
6.7%
95871
6.8%
106062
7.1%
ValueCountFrequency (%)
126205
7.2%
116108
7.1%
106062
7.1%
95871
6.8%
85778
6.7%
75779
6.7%
65610
6.5%
58964
10.4%
48917
10.4%
38974
10.5%

grupo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.837196
Minimum120
Maximum5011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:02.270401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile120
Q1120
median120
Q3225
95-th percentile701
Maximum5011
Range4891
Interquartile range (IQR)105

Descriptive statistics

Standard deviation166.0322885
Coefficient of variation (CV)0.8700205828
Kurtosis201.5684115
Mean190.837196
Median Absolute Deviation (MAD)0
Skewness8.717453626
Sum16377839
Variance27566.72083
MonotonicityNot monotonic
2022-07-01T23:37:02.558235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12050673
59.0%
2257694
 
9.0%
1717555
 
8.8%
2512511
 
2.9%
7012119
 
2.5%
7031909
 
2.2%
1311753
 
2.0%
7021579
 
1.8%
1251548
 
1.8%
2531365
 
1.6%
Other values (16)7115
 
8.3%
ValueCountFrequency (%)
12050673
59.0%
12187
 
0.1%
1251548
 
1.8%
1311753
 
2.0%
149607
 
0.7%
17029
 
< 0.1%
1717555
 
8.8%
220447
 
0.5%
221962
 
1.1%
2257694
 
9.0%
ValueCountFrequency (%)
501124
 
< 0.1%
7031909
2.2%
7021579
1.8%
7012119
2.5%
323972
1.1%
322977
1.1%
3211
 
< 0.1%
26141
 
< 0.1%
260109
 
0.1%
2591352
1.6%

clase
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
LONAS
62252 
PVC
16012 
PEGADO EN FRIO
 
5607
ARGYLL
 
1950

Length

Max length14
Median length5
Mean length5.237575885
Min length3

Characters and Unicode

Total characters449494
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLONAS
2nd rowLONAS
3rd rowLONAS
4th rowLONAS
5th rowLONAS

Common Values

ValueCountFrequency (%)
LONAS62252
72.5%
PVC16012
 
18.7%
PEGADO EN FRIO5607
 
6.5%
ARGYLL1950
 
2.3%

Length

2022-07-01T23:37:02.915031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T23:37:03.273114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
lonas62252
64.2%
pvc16012
 
16.5%
pegado5607
 
5.8%
en5607
 
5.8%
frio5607
 
5.8%
argyll1950
 
2.0%

Most occurring characters

ValueCountFrequency (%)
O73466
16.3%
A69809
15.5%
N67859
15.1%
L66152
14.7%
S62252
13.8%
P21619
 
4.8%
V16012
 
3.6%
C16012
 
3.6%
E11214
 
2.5%
11214
 
2.5%
Other values (6)33885
7.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter438280
97.5%
Space Separator11214
 
2.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O73466
16.8%
A69809
15.9%
N67859
15.5%
L66152
15.1%
S62252
14.2%
P21619
 
4.9%
V16012
 
3.7%
C16012
 
3.7%
E11214
 
2.6%
G7557
 
1.7%
Other values (5)26328
 
6.0%
Space Separator
ValueCountFrequency (%)
11214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin438280
97.5%
Common11214
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O73466
16.8%
A69809
15.9%
N67859
15.5%
L66152
15.1%
S62252
14.2%
P21619
 
4.9%
V16012
 
3.7%
C16012
 
3.7%
E11214
 
2.6%
G7557
 
1.7%
Other values (5)26328
 
6.0%
Common
ValueCountFrequency (%)
11214
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII449494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O73466
16.3%
A69809
15.5%
N67859
15.1%
L66152
14.7%
S62252
13.8%
P21619
 
4.8%
V16012
 
3.6%
C16012
 
3.6%
E11214
 
2.5%
11214
 
2.5%
Other values (6)33885
7.5%

subgrupo
Categorical

HIGH CARDINALITY

Distinct2301
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
FEMINELA ESTAMPADA
 
1288
BOTA TIFFANY ESTAMPADA
 
939
MACHA ESTAMPADA
 
493
MACHITA ESTAMP JR 29-34
 
351
SONTRA 2018 MEN
 
240
Other values (2296)
82510 

Length

Max length45
Median length37
Mean length15.66303119
Min length6

Characters and Unicode

Total characters1344217
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.1%

Sample

1st row360 FLIP MEN
2nd row360 FLIP MEN
3rd row50-50 MEN
4th row50-50 MEN
5th rowABDEL BOY

Common Values

ValueCountFrequency (%)
FEMINELA ESTAMPADA1288
 
1.5%
BOTA TIFFANY ESTAMPADA939
 
1.1%
MACHA ESTAMPADA493
 
0.6%
MACHITA ESTAMP JR 29-34351
 
0.4%
SONTRA 2018 MEN240
 
0.3%
SONTRA 2018 BOY239
 
0.3%
DISCOVERY BAJO LINE MEN226
 
0.3%
DISCOVERY BAJO COLORES MEN196
 
0.2%
DISCOVERY BAJO VINTAGE MEN190
 
0.2%
DISCOVERY ALTO COLORES JUNIOR189
 
0.2%
Other values (2291)81470
94.9%

Length

2022-07-01T23:37:03.502105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
men32470
 
13.4%
boy21454
 
8.8%
bota9128
 
3.8%
jun7843
 
3.2%
discovery5848
 
2.4%
inf4613
 
1.9%
bajo4086
 
1.7%
22-253893
 
1.6%
26-323252
 
1.3%
estampada2765
 
1.1%
Other values (1238)147199
60.7%

Most occurring characters

ValueCountFrequency (%)
157145
 
11.7%
A131502
 
9.8%
O110227
 
8.2%
E94102
 
7.0%
N92314
 
6.9%
I76463
 
5.7%
R64156
 
4.8%
M62044
 
4.6%
L55597
 
4.1%
B50220
 
3.7%
Other values (34)450447
33.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1105648
82.3%
Space Separator157145
 
11.7%
Decimal Number53870
 
4.0%
Dash Punctuation14477
 
1.1%
Other Punctuation12830
 
1.0%
Control247
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A131502
11.9%
O110227
 
10.0%
E94102
 
8.5%
N92314
 
8.3%
I76463
 
6.9%
R64156
 
5.8%
M62044
 
5.6%
L55597
 
5.0%
B50220
 
4.5%
S46405
 
4.2%
Other values (18)322618
29.2%
Decimal Number
ValueCountFrequency (%)
226180
48.6%
39248
 
17.2%
64774
 
8.9%
54222
 
7.8%
12618
 
4.9%
02423
 
4.5%
41555
 
2.9%
81407
 
2.6%
7821
 
1.5%
9622
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/9942
77.5%
.1625
 
12.7%
"1263
 
9.8%
Space Separator
ValueCountFrequency (%)
157145
100.0%
Dash Punctuation
ValueCountFrequency (%)
-14477
100.0%
Control
ValueCountFrequency (%)
247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1105648
82.3%
Common238569
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A131502
11.9%
O110227
 
10.0%
E94102
 
8.5%
N92314
 
8.3%
I76463
 
6.9%
R64156
 
5.8%
M62044
 
5.6%
L55597
 
5.0%
B50220
 
4.5%
S46405
 
4.2%
Other values (18)322618
29.2%
Common
ValueCountFrequency (%)
157145
65.9%
226180
 
11.0%
-14477
 
6.1%
/9942
 
4.2%
39248
 
3.9%
64774
 
2.0%
54222
 
1.8%
12618
 
1.1%
02423
 
1.0%
.1625
 
0.7%
Other values (6)5915
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1344140
> 99.9%
None77
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
157145
 
11.7%
A131502
 
9.8%
O110227
 
8.2%
E94102
 
7.0%
N92314
 
6.9%
I76463
 
5.7%
R64156
 
4.8%
M62044
 
4.6%
L55597
 
4.1%
B50220
 
3.7%
Other values (32)450370
33.5%
None
ValueCountFrequency (%)
Ñ74
96.1%
É3
 
3.9%

Linea
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
Discovery
50673 
Botas Disney
7694 
Deportivo
7555 
Botas Machita
 
2511
Coleg. Croydon Cuero
 
2119
Other values (21)
15269 

Length

Max length32
Median length9
Mean length11.01611494
Min length5

Characters and Unicode

Total characters945414
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDiscovery
2nd rowDiscovery
3rd rowDiscovery
4th rowDiscovery
5th rowDiscovery

Common Values

ValueCountFrequency (%)
Discovery50673
59.0%
Botas Disney7694
 
9.0%
Deportivo7555
 
8.8%
Botas Machita2511
 
2.9%
Coleg. Croydon Cuero2119
 
2.5%
Coleg. Croydon Pvc1909
 
2.2%
Royal Lona1753
 
2.0%
Discovery Peg. En Fr1579
 
1.8%
Disney Convencional1548
 
1.8%
Botas Feminela Estam1365
 
1.6%
Other values (16)7115
 
8.3%

Length

2022-07-01T23:37:03.827548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
discovery52252
41.3%
botas15448
 
12.2%
disney9242
 
7.3%
deportivo7555
 
6.0%
coleg4028
 
3.2%
croydon4028
 
3.2%
machita3273
 
2.6%
pvc2356
 
1.9%
cuero2119
 
1.7%
lona1782
 
1.4%
Other values (29)24289
19.2%

Most occurring characters

ValueCountFrequency (%)
o109803
11.6%
e84141
 
8.9%
s81155
 
8.6%
i78763
 
8.3%
y70664
 
7.5%
D70468
 
7.5%
r69883
 
7.4%
v64318
 
6.8%
c61950
 
6.6%
41166
 
4.4%
Other values (29)213103
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter772269
81.7%
Uppercase Letter126372
 
13.4%
Space Separator41166
 
4.4%
Other Punctuation5607
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o109803
14.2%
e84141
10.9%
s81155
10.5%
i78763
10.2%
y70664
9.2%
r69883
9.0%
v64318
8.3%
c61950
8.0%
a40499
 
5.2%
t31229
 
4.0%
Other values (12)79864
10.3%
Uppercase Letter
ValueCountFrequency (%)
D70468
55.8%
B16918
 
13.4%
C13410
 
10.6%
E5161
 
4.1%
P3976
 
3.1%
M3766
 
3.0%
F3237
 
2.6%
L1782
 
1.4%
R1753
 
1.4%
T1548
 
1.2%
Other values (5)4353
 
3.4%
Space Separator
ValueCountFrequency (%)
41166
100.0%
Other Punctuation
ValueCountFrequency (%)
.5607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin898641
95.1%
Common46773
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o109803
12.2%
e84141
9.4%
s81155
9.0%
i78763
8.8%
y70664
7.9%
D70468
7.8%
r69883
7.8%
v64318
 
7.2%
c61950
 
6.9%
a40499
 
4.5%
Other values (27)166997
18.6%
Common
ValueCountFrequency (%)
41166
88.0%
.5607
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII945414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o109803
11.6%
e84141
 
8.9%
s81155
 
8.6%
i78763
 
8.3%
y70664
 
7.5%
D70468
 
7.5%
r69883
 
7.4%
v64318
 
6.8%
c61950
 
6.6%
41166
 
4.4%
Other values (29)213103
22.5%

Ref.
Categorical

HIGH CARDINALITY

Distinct2313
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
5900090
 
145
5900060
 
144
7220060
 
116
7221050
 
116
J594040
 
116
Other values (2308)
85184 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters600747
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowAF20050
2nd rowAF20080
3rd rowAD90019
4th rowAD90020
5th rowAE55020

Common Values

ValueCountFrequency (%)
5900090145
 
0.2%
5900060144
 
0.2%
7220060116
 
0.1%
7221050116
 
0.1%
J594040116
 
0.1%
AD87060116
 
0.1%
7230050116
 
0.1%
J604040116
 
0.1%
AK74090116
 
0.1%
7220090116
 
0.1%
Other values (2303)84604
98.6%

Length

2022-07-01T23:37:04.014054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5900090145
 
0.2%
5900060144
 
0.2%
7220060116
 
0.1%
j594040116
 
0.1%
ad87060116
 
0.1%
7230050116
 
0.1%
j604040116
 
0.1%
ak74090116
 
0.1%
7220090116
 
0.1%
7221050116
 
0.1%
Other values (2303)84604
98.6%

Most occurring characters

ValueCountFrequency (%)
0182269
30.3%
651171
 
8.5%
942752
 
7.1%
A41139
 
6.8%
237604
 
6.3%
534074
 
5.7%
431361
 
5.2%
827377
 
4.6%
726560
 
4.4%
324483
 
4.1%
Other values (26)101957
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number482044
80.2%
Uppercase Letter118703
 
19.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A41139
34.7%
C9756
 
8.2%
B9178
 
7.7%
O9050
 
7.6%
G6253
 
5.3%
Q5083
 
4.3%
E4372
 
3.7%
J4361
 
3.7%
H3576
 
3.0%
K3284
 
2.8%
Other values (16)22651
19.1%
Decimal Number
ValueCountFrequency (%)
0182269
37.8%
651171
 
10.6%
942752
 
8.9%
237604
 
7.8%
534074
 
7.1%
431361
 
6.5%
827377
 
5.7%
726560
 
5.5%
324483
 
5.1%
124393
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common482044
80.2%
Latin118703
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A41139
34.7%
C9756
 
8.2%
B9178
 
7.7%
O9050
 
7.6%
G6253
 
5.3%
Q5083
 
4.3%
E4372
 
3.7%
J4361
 
3.7%
H3576
 
3.0%
K3284
 
2.8%
Other values (16)22651
19.1%
Common
ValueCountFrequency (%)
0182269
37.8%
651171
 
10.6%
942752
 
8.9%
237604
 
7.8%
534074
 
7.1%
431361
 
6.5%
827377
 
5.7%
726560
 
5.5%
324483
 
5.1%
124393
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII600747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0182269
30.3%
651171
 
8.5%
942752
 
7.1%
A41139
 
6.8%
237604
 
6.3%
534074
 
5.7%
431361
 
5.2%
827377
 
4.6%
726560
 
4.4%
324483
 
4.1%
Other values (26)101957
17.0%

Descripcion
Categorical

HIGH CARDINALITY

Distinct2242
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
DISCOVERY ALTO ROJO CROYDON
 
169
CHAMPION BLANCO CROYDON
 
145
MACHITA NEGRO CROYDON
 
145
MACHITA AZUL CROYDON
 
144
DEPORTIVO BAJO COLORS BLANCO
 
144
Other values (2237)
85074 

Length

Max length33
Median length28
Mean length22.25653395
Min length8

Characters and Unicode

Total characters1910078
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st row360 FLIP ROJO CROYDON
2nd row360 FLIP VERDE CROYDON
3rd row50-50 GRIS OSCURO CROYDON
4th row50-50 GRIS CROYDON
5th rowABDEL GRIS CROYDON

Common Values

ValueCountFrequency (%)
DISCOVERY ALTO ROJO CROYDON169
 
0.2%
CHAMPION BLANCO CROYDON145
 
0.2%
MACHITA NEGRO CROYDON145
 
0.2%
MACHITA AZUL CROYDON144
 
0.2%
DEPORTIVO BAJO COLORS BLANCO144
 
0.2%
CHAMPION NEGRO CROYDON143
 
0.2%
DEPORTIVO ALTO NEGRO CROYDON140
 
0.2%
ENDOR FUCSIA DISNEY116
 
0.1%
DISCOVERY BAJO ROJO CROYDON116
 
0.1%
DISCOVERY ALTO BLANCO ROJO116
 
0.1%
Other values (2232)84443
98.4%

Length

2022-07-01T23:37:04.339605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
croydon64077
21.7%
azul18354
 
6.2%
negro14642
 
5.0%
gris8905
 
3.0%
disney8339
 
2.8%
rojo6161
 
2.1%
blanco6141
 
2.1%
fucsia5757
 
2.0%
cafe5495
 
1.9%
rosado5249
 
1.8%
Other values (1264)152073
51.5%

Most occurring characters

ValueCountFrequency (%)
O254249
13.3%
209625
11.0%
R162898
 
8.5%
A159559
 
8.4%
N139406
 
7.3%
C116194
 
6.1%
D107372
 
5.6%
I99259
 
5.2%
E94586
 
5.0%
Y88727
 
4.6%
Other values (35)478203
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1689023
88.4%
Space Separator209625
 
11.0%
Decimal Number8557
 
0.4%
Dash Punctuation1837
 
0.1%
Other Punctuation1022
 
0.1%
Open Punctuation7
 
< 0.1%
Close Punctuation7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O254249
15.1%
R162898
9.6%
A159559
9.4%
N139406
 
8.3%
C116194
 
6.9%
D107372
 
6.4%
I99259
 
5.9%
E94586
 
5.6%
Y88727
 
5.3%
L79990
 
4.7%
Other values (18)386783
22.9%
Decimal Number
ValueCountFrequency (%)
22670
31.2%
11734
20.3%
01713
20.0%
3653
 
7.6%
8596
 
7.0%
4479
 
5.6%
5443
 
5.2%
9165
 
1.9%
675
 
0.9%
729
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.552
54.0%
"257
25.1%
/213
 
20.8%
Space Separator
ValueCountFrequency (%)
209625
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1837
100.0%
Open Punctuation
ValueCountFrequency (%)
(7
100.0%
Close Punctuation
ValueCountFrequency (%)
)7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1689023
88.4%
Common221055
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
O254249
15.1%
R162898
9.6%
A159559
9.4%
N139406
 
8.3%
C116194
 
6.9%
D107372
 
6.4%
I99259
 
5.9%
E94586
 
5.6%
Y88727
 
5.3%
L79990
 
4.7%
Other values (18)386783
22.9%
Common
ValueCountFrequency (%)
209625
94.8%
22670
 
1.2%
-1837
 
0.8%
11734
 
0.8%
01713
 
0.8%
3653
 
0.3%
8596
 
0.3%
.552
 
0.2%
4479
 
0.2%
5443
 
0.2%
Other values (7)753
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1910021
> 99.9%
None57
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O254249
13.3%
209625
11.0%
R162898
 
8.5%
A159559
 
8.4%
N139406
 
7.3%
C116194
 
6.1%
D107372
 
5.6%
I99259
 
5.2%
E94586
 
5.0%
Y88727
 
4.6%
Other values (33)478146
25.0%
None
ValueCountFrequency (%)
Ð45
78.9%
Ñ12
 
21.1%

q ini
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3743
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.5138369
Minimum0
Maximum96776
Zeros1417
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:04.691730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median30
Q3184
95-th percentile1053
Maximum96776
Range96776
Interquartile range (IQR)178

Descriptive statistics

Standard deviation1374.932447
Coefficient of variation (CV)4.700401396
Kurtosis1069.893443
Mean292.5138369
Median Absolute Deviation (MAD)29
Skewness24.54171688
Sum25103830
Variance1890439.235
MonotonicityNot monotonic
2022-07-01T23:37:04.932180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17709
 
9.0%
24464
 
5.2%
33303
 
3.8%
42254
 
2.6%
52145
 
2.5%
61937
 
2.3%
71576
 
1.8%
81490
 
1.7%
01417
 
1.7%
91302
 
1.5%
Other values (3733)58224
67.8%
ValueCountFrequency (%)
01417
 
1.7%
17709
9.0%
24464
5.2%
33303
3.8%
42254
 
2.6%
52145
 
2.5%
61937
 
2.3%
71576
 
1.8%
81490
 
1.7%
91302
 
1.5%
ValueCountFrequency (%)
967761
< 0.1%
884321
< 0.1%
776061
< 0.1%
677631
< 0.1%
670131
< 0.1%
669301
< 0.1%
610371
< 0.1%
597301
< 0.1%
504861
< 0.1%
483921
< 0.1%

$ ini
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct40623
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4973147.279
Minimum0
Maximum444017599
Zeros1417
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:05.173068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19465
Q1115355
median663789
Q33868017
95-th percentile21099855
Maximum444017599
Range444017599
Interquartile range (IQR)3752662

Descriptive statistics

Standard deviation15529857.3
Coefficient of variation (CV)3.122742285
Kurtosis142.815814
Mean4973147.279
Median Absolute Deviation (MAD)638827
Skewness9.621702355
Sum4.268004726 × 1011
Variance2.411764677 × 1014
MonotonicityNot monotonic
2022-07-01T23:37:05.406329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01417
 
1.7%
8630177
 
0.2%
17728111
 
0.1%
1726099
 
0.1%
886496
 
0.1%
2147980
 
0.1%
1268972
 
0.1%
2262768
 
0.1%
2302067
 
0.1%
16627866
 
0.1%
Other values (40613)83568
97.4%
ValueCountFrequency (%)
01417
1.7%
33081
 
< 0.1%
33992
 
< 0.1%
67071
 
< 0.1%
67982
 
< 0.1%
68624
 
< 0.1%
740427
 
< 0.1%
74281
 
< 0.1%
764229
 
< 0.1%
79204
 
< 0.1%
ValueCountFrequency (%)
4440175991
< 0.1%
4367500881
< 0.1%
3998255881
< 0.1%
3990936161
< 0.1%
3964513221
< 0.1%
3803778481
< 0.1%
3783574261
< 0.1%
3722829341
< 0.1%
3673981961
< 0.1%
3653183161
< 0.1%

Q pro
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1749
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.67644283
Minimum-938
Maximum87258
Zeros79449
Zeros (%)92.6%
Negative181
Negative (%)0.2%
Memory size670.6 KiB
2022-07-01T23:37:05.608937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-938
5-th percentile0
Q10
median0
Q30
95-th percentile132
Maximum87258
Range88196
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1235.781202
Coefficient of variation (CV)16.1168301
Kurtosis2334.076263
Mean76.67644283
Median Absolute Deviation (MAD)0
Skewness43.5957974
Sum6580449
Variance1527155.179
MonotonicityNot monotonic
2022-07-01T23:37:05.795208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
079449
92.6%
1189
 
0.2%
-1102
 
0.1%
1282
 
0.1%
2470
 
0.1%
261
 
0.1%
3660
 
0.1%
10858
 
0.1%
12053
 
0.1%
4850
 
0.1%
Other values (1739)5647
 
6.6%
ValueCountFrequency (%)
-9381
< 0.1%
-7591
< 0.1%
-6121
< 0.1%
-6051
< 0.1%
-5121
< 0.1%
-3961
< 0.1%
-3551
< 0.1%
-3141
< 0.1%
-2941
< 0.1%
-2881
< 0.1%
ValueCountFrequency (%)
872581
< 0.1%
830941
< 0.1%
815021
< 0.1%
773281
< 0.1%
772021
< 0.1%
757821
< 0.1%
726171
< 0.1%
704491
< 0.1%
694331
< 0.1%
670361
< 0.1%

$pro
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5980
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean925309.3726
Minimum-9546026
Maximum616425735
Zeros79450
Zeros (%)92.6%
Negative181
Negative (%)0.2%
Memory size670.6 KiB
2022-07-01T23:37:06.083377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-9546026
5-th percentile0
Q10
median0
Q30
95-th percentile2339736
Maximum616425735
Range625971761
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8850189.769
Coefficient of variation (CV)9.564573785
Kurtosis1121.855491
Mean925309.3726
Median Absolute Deviation (MAD)0
Skewness27.24311778
Sum7.941097567 × 1010
Variance7.832585895 × 1013
MonotonicityNot monotonic
2022-07-01T23:37:06.275150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
079450
92.6%
2244488
 
< 0.1%
2096887
 
< 0.1%
4193767
 
< 0.1%
-88646
 
< 0.1%
1048446
 
< 0.1%
5688005
 
< 0.1%
3412805
 
< 0.1%
9920884
 
< 0.1%
10916404
 
< 0.1%
Other values (5970)6319
 
7.4%
ValueCountFrequency (%)
-95460261
< 0.1%
-93758401
< 0.1%
-82913161
< 0.1%
-68933701
< 0.1%
-46710591
< 0.1%
-38155041
< 0.1%
-30225251
< 0.1%
-28573861
< 0.1%
-26813441
< 0.1%
-23189401
< 0.1%
ValueCountFrequency (%)
6164257351
< 0.1%
5999523311
< 0.1%
3937953541
< 0.1%
3862305551
< 0.1%
3793404261
< 0.1%
3750032221
< 0.1%
3678185261
< 0.1%
3518134741
< 0.1%
3489812641
< 0.1%
3484126261
< 0.1%

Q com
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
0.0
85821 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters257463
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.085821
100.0%

Length

2022-07-01T23:37:06.473037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T23:37:06.643664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.085821
100.0%

Most occurring characters

ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number171642
66.7%
Other Punctuation85821
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0171642
100.0%
Other Punctuation
ValueCountFrequency (%)
.85821
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common257463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII257463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

$ com
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
0.0
85821 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters257463
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.085821
100.0%

Length

2022-07-01T23:37:06.803442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T23:37:07.688755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.085821
100.0%

Most occurring characters

ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number171642
66.7%
Other Punctuation85821
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0171642
100.0%
Other Punctuation
ValueCountFrequency (%)
.85821
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common257463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII257463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Q ven
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1695
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.66286806
Minimum0
Maximum88448
Zeros47591
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:07.874481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile151
Maximum88448
Range88448
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1189.051874
Coefficient of variation (CV)14.74100665
Kurtosis2595.051506
Mean80.66286806
Median Absolute Deviation (MAD)0
Skewness45.70429977
Sum6922568
Variance1413844.36
MonotonicityNot monotonic
2022-07-01T23:37:08.152721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047591
55.5%
15114
 
6.0%
22938
 
3.4%
32099
 
2.4%
41692
 
2.0%
51347
 
1.6%
61234
 
1.4%
71004
 
1.2%
8870
 
1.0%
9778
 
0.9%
Other values (1685)21154
24.6%
ValueCountFrequency (%)
047591
55.5%
15114
 
6.0%
22938
 
3.4%
32099
 
2.4%
41692
 
2.0%
51347
 
1.6%
61234
 
1.4%
71004
 
1.2%
8870
 
1.0%
9778
 
0.9%
ValueCountFrequency (%)
884481
< 0.1%
883581
< 0.1%
848531
< 0.1%
830861
< 0.1%
757331
< 0.1%
735641
< 0.1%
702211
< 0.1%
680261
< 0.1%
603641
< 0.1%
592961
< 0.1%

$ ven
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct24842
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean983627.459
Minimum0
Maximum547298378
Zeros47591
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:08.589805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3193200
95-th percentile2938494
Maximum547298378
Range547298378
Interquartile range (IQR)193200

Descriptive statistics

Standard deviation8180342.634
Coefficient of variation (CV)8.316504953
Kurtosis1069.255942
Mean983627.459
Median Absolute Deviation (MAD)0
Skewness27.38079609
Sum8.441589216 × 1010
Variance6.691800562 × 1013
MonotonicityNot monotonic
2022-07-01T23:37:08.843792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047591
55.5%
886466
 
0.1%
1772852
 
0.1%
2659243
 
0.1%
2375439
 
< 0.1%
1247230
 
< 0.1%
2272828
 
< 0.1%
3545627
 
< 0.1%
5318427
 
< 0.1%
1855525
 
< 0.1%
Other values (24832)37893
44.2%
ValueCountFrequency (%)
047591
55.5%
323312
 
< 0.1%
33089
 
< 0.1%
339922
 
< 0.1%
60681
 
< 0.1%
63561
 
< 0.1%
65098
 
< 0.1%
65361
 
< 0.1%
65421
 
< 0.1%
65741
 
< 0.1%
ValueCountFrequency (%)
5472983781
< 0.1%
3991658241
< 0.1%
3987596541
< 0.1%
3855835011
< 0.1%
3838912531
< 0.1%
3796058091
< 0.1%
3749671181
< 0.1%
3747207961
< 0.1%
3735708031
< 0.1%
3478384811
< 0.1%

Q dev
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct352
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.249181436
Minimum0
Maximum8972
Zeros64985
Zeros (%)75.7%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:09.054144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum8972
Range8972
Interquartile range (IQR)0

Descriptive statistics

Standard deviation66.23065177
Coefficient of variation (CV)15.58668482
Kurtosis8653.27872
Mean4.249181436
Median Absolute Deviation (MAD)0
Skewness79.99155308
Sum364669
Variance4386.499234
MonotonicityNot monotonic
2022-07-01T23:37:09.240667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064985
75.7%
15442
 
6.3%
22830
 
3.3%
31791
 
2.1%
41341
 
1.6%
51036
 
1.2%
6797
 
0.9%
7720
 
0.8%
8590
 
0.7%
9492
 
0.6%
Other values (342)5797
 
6.8%
ValueCountFrequency (%)
064985
75.7%
15442
 
6.3%
22830
 
3.3%
31791
 
2.1%
41341
 
1.6%
51036
 
1.2%
6797
 
0.9%
7720
 
0.8%
8590
 
0.7%
9492
 
0.6%
ValueCountFrequency (%)
89721
< 0.1%
76441
< 0.1%
73351
< 0.1%
52471
< 0.1%
44461
< 0.1%
29881
< 0.1%
26001
< 0.1%
24131
< 0.1%
22171
< 0.1%
22061
< 0.1%

$ dev
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct10198
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74015.72168
Minimum0
Maximum88609248
Zeros64985
Zeros (%)75.7%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:09.459818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile283135
Maximum88609248
Range88609248
Interquartile range (IQR)0

Descriptive statistics

Standard deviation833497.6183
Coefficient of variation (CV)11.26108885
Kurtosis5076.555957
Mean74015.72168
Median Absolute Deviation (MAD)0
Skewness59.99138643
Sum6352103250
Variance6.947182798 × 1011
MonotonicityNot monotonic
2022-07-01T23:37:09.779608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064985
75.7%
886480
 
0.1%
957050
 
0.1%
1247242
 
< 0.1%
935233
 
< 0.1%
339930
 
< 0.1%
1772830
 
< 0.1%
2480730
 
< 0.1%
2419629
 
< 0.1%
2659226
 
< 0.1%
Other values (10188)20486
 
23.9%
ValueCountFrequency (%)
064985
75.7%
15171
 
< 0.1%
30341
 
< 0.1%
32331
 
< 0.1%
330814
 
< 0.1%
339930
 
< 0.1%
35167
 
< 0.1%
36068
 
< 0.1%
45512
 
< 0.1%
52371
 
< 0.1%
ValueCountFrequency (%)
886092481
< 0.1%
881857881
< 0.1%
850273201
< 0.1%
511000001
< 0.1%
478160001
< 0.1%
467564011
< 0.1%
465784501
< 0.1%
406566241
< 0.1%
400675471
< 0.1%
376404141
< 0.1%

Q tra
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
0.0
85821 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters257463
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.085821
100.0%

Length

2022-07-01T23:37:10.014099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T23:37:10.189920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.085821
100.0%

Most occurring characters

ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number171642
66.7%
Other Punctuation85821
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0171642
100.0%
Other Punctuation
ValueCountFrequency (%)
.85821
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common257463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII257463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0171642
66.7%
.85821
33.3%

$ tra
Real number (ℝ)

SKEWED
ZEROS

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.609839084
Minimum-537698
Maximum697771
Zeros85567
Zeros (%)99.7%
Negative164
Negative (%)0.2%
Memory size670.6 KiB
2022-07-01T23:37:10.430211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-537698
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum697771
Range1235469
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3008.080567
Coefficient of variation (CV)1868.559782
Kurtosis45633.11541
Mean1.609839084
Median Absolute Deviation (MAD)0
Skewness78.88591598
Sum138158
Variance9048548.696
MonotonicityNot monotonic
2022-07-01T23:37:10.803676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
085567
99.7%
-145
 
0.1%
-229
 
< 0.1%
124
 
< 0.1%
28
 
< 0.1%
-68
 
< 0.1%
-38
 
< 0.1%
-77
 
< 0.1%
-47
 
< 0.1%
-115
 
< 0.1%
Other values (84)113
 
0.1%
ValueCountFrequency (%)
-5376981
< 0.1%
-134121
< 0.1%
-131931
< 0.1%
-74591
< 0.1%
-50941
< 0.1%
-40001
< 0.1%
-19941
< 0.1%
-19821
< 0.1%
-1111
< 0.1%
-801
< 0.1%
ValueCountFrequency (%)
6977711
< 0.1%
78131
< 0.1%
27971
< 0.1%
20531
< 0.1%
19741
< 0.1%
9881
< 0.1%
9501
< 0.1%
8341
< 0.1%
7591
< 0.1%
7181
< 0.1%

Q s/e
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct396
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3845562275
Minimum-2464
Maximum2710
Zeros67469
Zeros (%)78.6%
Negative12015
Negative (%)14.0%
Memory size670.6 KiB
2022-07-01T23:37:11.061241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2464
5-th percentile-3
Q10
median0
Q30
95-th percentile1
Maximum2710
Range5174
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.84698072
Coefficient of variation (CV)-67.21248771
Kurtosis4772.61083
Mean-0.3845562275
Median Absolute Deviation (MAD)0
Skewness12.50226854
Sum-33003
Variance668.0664122
MonotonicityNot monotonic
2022-07-01T23:37:11.251570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067469
78.6%
-14989
 
5.8%
12721
 
3.2%
-21834
 
2.1%
-31021
 
1.2%
2960
 
1.1%
-4674
 
0.8%
3491
 
0.6%
-5463
 
0.5%
-6332
 
0.4%
Other values (386)4867
 
5.7%
ValueCountFrequency (%)
-24641
< 0.1%
-22801
< 0.1%
-11181
< 0.1%
-9341
< 0.1%
-8271
< 0.1%
-7561
< 0.1%
-7331
< 0.1%
-6581
< 0.1%
-6461
< 0.1%
-5551
< 0.1%
ValueCountFrequency (%)
27101
< 0.1%
22651
< 0.1%
22561
< 0.1%
14331
< 0.1%
9591
< 0.1%
8761
< 0.1%
8281
< 0.1%
8241
< 0.1%
6491
< 0.1%
6071
< 0.1%

$ s/e
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10926
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31471.43907
Minimum-172424444
Maximum171053804
Zeros66839
Zeros (%)77.9%
Negative12635
Negative (%)14.7%
Memory size670.6 KiB
2022-07-01T23:37:11.449178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-172424444
5-th percentile-91998
Q10
median0
Q30
95-th percentile24633
Maximum171053804
Range343478248
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1037279.342
Coefficient of variation (CV)-32.95938706
Kurtosis17743.83073
Mean-31471.43907
Median Absolute Deviation (MAD)0
Skewness-4.445941872
Sum-2700910373
Variance1.075948433 × 1012
MonotonicityNot monotonic
2022-07-01T23:37:11.731501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
066839
77.9%
-886496
 
0.1%
886461
 
0.1%
-1772844
 
0.1%
1247233
 
< 0.1%
-990129
 
< 0.1%
-873729
 
< 0.1%
-962527
 
< 0.1%
339926
 
< 0.1%
-742825
 
< 0.1%
Other values (10916)18612
 
21.7%
ValueCountFrequency (%)
-1724244441
< 0.1%
-524637301
< 0.1%
-354220791
< 0.1%
-310650481
< 0.1%
-293597561
< 0.1%
-289470721
< 0.1%
-285654551
< 0.1%
-283590271
< 0.1%
-254430001
< 0.1%
-247122721
< 0.1%
ValueCountFrequency (%)
1710538041
< 0.1%
457719841
< 0.1%
318370801
< 0.1%
290741371
< 0.1%
255133341
< 0.1%
217367741
< 0.1%
146911141
< 0.1%
124795801
< 0.1%
123917501
< 0.1%
99861631
< 0.1%

q fin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct3726
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.3920253
Minimum0
Maximum96776
Zeros491
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:11.987558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median31
Q3184
95-th percentile1049
Maximum96776
Range96776
Interquartile range (IQR)178

Descriptive statistics

Standard deviation1384.806616
Coefficient of variation (CV)4.73612991
Kurtosis1093.961883
Mean292.3920253
Median Absolute Deviation (MAD)29
Skewness24.96065307
Sum25093376
Variance1917689.365
MonotonicityNot monotonic
2022-07-01T23:37:12.339565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17832
 
9.1%
24558
 
5.3%
33366
 
3.9%
42334
 
2.7%
52196
 
2.6%
62011
 
2.3%
71602
 
1.9%
81511
 
1.8%
91342
 
1.6%
121285
 
1.5%
Other values (3716)57784
67.3%
ValueCountFrequency (%)
0491
 
0.6%
17832
9.1%
24558
5.3%
33366
3.9%
42334
 
2.7%
52196
 
2.6%
62011
 
2.3%
71602
 
1.9%
81511
 
1.8%
91342
 
1.6%
ValueCountFrequency (%)
967761
< 0.1%
884321
< 0.1%
776061
< 0.1%
692891
< 0.1%
677631
< 0.1%
670131
< 0.1%
669301
< 0.1%
610371
< 0.1%
597301
< 0.1%
504861
< 0.1%

$ fin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40677
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4957203.527
Minimum0
Maximum444017599
Zeros491
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:12.736425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21169
Q1121620
median669383
Q33862542
95-th percentile21071892
Maximum444017599
Range444017599
Interquartile range (IQR)3740922

Descriptive statistics

Standard deviation15454266.34
Coefficient of variation (CV)3.117537187
Kurtosis145.7566332
Mean4957203.527
Median Absolute Deviation (MAD)642241
Skewness9.70044781
Sum4.254321639 × 1011
Variance2.388343482 × 1014
MonotonicityNot monotonic
2022-07-01T23:37:12.942443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0491
 
0.6%
8630176
 
0.2%
17728118
 
0.1%
1726099
 
0.1%
886497
 
0.1%
2147978
 
0.1%
1268973
 
0.1%
16627867
 
0.1%
2302067
 
0.1%
2262766
 
0.1%
Other values (40667)84489
98.4%
ValueCountFrequency (%)
0491
0.6%
33993
 
< 0.1%
66161
 
< 0.1%
67981
 
< 0.1%
68625
 
< 0.1%
740427
 
< 0.1%
74282
 
< 0.1%
764229
 
< 0.1%
79205
 
< 0.1%
793711
 
< 0.1%
ValueCountFrequency (%)
4440175991
< 0.1%
4367500881
< 0.1%
3990936161
< 0.1%
3964513221
< 0.1%
3803778481
< 0.1%
3783574261
< 0.1%
3722829341
< 0.1%
3673981961
< 0.1%
3653183161
< 0.1%
3619772941
< 0.1%

Q ven2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1643
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.41368663
Minimum0
Maximum88186
Zeros48483
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size670.6 KiB
2022-07-01T23:37:13.286036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile135
Maximum88186
Range88186
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1163.450982
Coefficient of variation (CV)15.2256884
Kurtosis2625.363251
Mean76.41368663
Median Absolute Deviation (MAD)0
Skewness46.05782339
Sum6557899
Variance1353618.188
MonotonicityNot monotonic
2022-07-01T23:37:13.603398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048483
56.5%
15349
 
6.2%
23009
 
3.5%
32078
 
2.4%
41679
 
2.0%
51349
 
1.6%
61207
 
1.4%
7995
 
1.2%
8864
 
1.0%
9737
 
0.9%
Other values (1633)20071
23.4%
ValueCountFrequency (%)
048483
56.5%
15349
 
6.2%
23009
 
3.5%
32078
 
2.4%
41679
 
2.0%
51349
 
1.6%
61207
 
1.4%
7995
 
1.2%
8864
 
1.0%
9737
 
0.9%
ValueCountFrequency (%)
881861
< 0.1%
831821
< 0.1%
831111
< 0.1%
827961
< 0.1%
750111
< 0.1%
723601
< 0.1%
700911
< 0.1%
677911
< 0.1%
570901
< 0.1%
568081
< 0.1%

$ ven3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct24043
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean909611.7373
Minimum-3108
Maximum546344965
Zeros48455
Zeros (%)56.5%
Negative11
Negative (%)< 0.1%
Memory size670.6 KiB
2022-07-01T23:37:13.958498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3108
5-th percentile0
Q10
median0
Q3166074
95-th percentile2622966
Maximum546344965
Range546348073
Interquartile range (IQR)166074

Descriptive statistics

Standard deviation7896262.05
Coefficient of variation (CV)8.680914863
Kurtosis1134.939538
Mean909611.7373
Median Absolute Deviation (MAD)0
Skewness28.24911266
Sum7.806378891 × 1010
Variance6.235095436 × 1013
MonotonicityNot monotonic
2022-07-01T23:37:14.266280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048455
56.5%
886467
 
0.1%
1772850
 
0.1%
2659243
 
0.1%
2375439
 
< 0.1%
2272832
 
< 0.1%
1247230
 
< 0.1%
3545629
 
< 0.1%
4432027
 
< 0.1%
2772525
 
< 0.1%
Other values (24033)37024
43.1%
ValueCountFrequency (%)
-31081
< 0.1%
-28501
< 0.1%
-21641
< 0.1%
-21601
< 0.1%
-21271
< 0.1%
-18181
< 0.1%
-16622
< 0.1%
-14981
< 0.1%
-9091
< 0.1%
-4681
< 0.1%
ValueCountFrequency (%)
5463449651
< 0.1%
3979834181
< 0.1%
3824857061
< 0.1%
3780422781
< 0.1%
3777972731
< 0.1%
3750799431
< 0.1%
3736583481
< 0.1%
3724306391
< 0.1%
3457055881
< 0.1%
3385246431
< 0.1%

fecha
Date

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.6 KiB
Minimum2019-01-01 00:00:00
Maximum2021-05-01 00:00:00
2022-07-01T23:37:14.564805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:37:14.784487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

Interactions

2022-07-01T23:36:48.653288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:52.968795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:59.816620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:06.612555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:13.824546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:19.995534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:26.504709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:32.950380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:38.951492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:45.516888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:51.589285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:57.692076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:03.726964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:09.779464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:16.437251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:22.522296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:29.175752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:35.700372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:42.395022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:49.025432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:53.402393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:00.167031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:07.052493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:14.191732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:20.408451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:26.887738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:33.234451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:39.336043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:45.866935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:51.931215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:57.976720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:03.999636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:10.156621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:16.724628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:22.939422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:29.645168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:36.038039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:42.779086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:49.416795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:53.780776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:00.563395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:07.475359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:14.641543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:20.879600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:27.249495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:33.576743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:39.682179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:46.164680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:52.245651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:58.294134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:04.292826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:11.034153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:17.019174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:23.303190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:29.935743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:36.322283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:43.127712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:49.704923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:54.265272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:00.977925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:07.843211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:14.928889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:21.161454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:27.621567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:33.899296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:40.085315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:46.438393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:52.598721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:58.631144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:04.625354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:11.389917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:17.418098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:23.721279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:30.338228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:36.642390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:43.520734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:50.003533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:54.559357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:01.331164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:08.277263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:15.218047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:21.444328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:27.956006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:34.177633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:40.407488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:46.715159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:52.946855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:58.878348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:04.893238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:11.708764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:17.864539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:24.098305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:30.705863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:36.940734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:43.793451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:50.270182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:54.818230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:01.642131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:08.655360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:15.744837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:21.858543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:28.271425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:34.464513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:40.734991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:47.032580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:53.249959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:59.178130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:05.226360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:11.954075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:18.254959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:24.439765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:31.020977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:37.271567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:44.078760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:50.608168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:55.214346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:01.981928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:08.961874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:16.059813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:22.208086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:28.653881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:34.789348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:41.001290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:47.298300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:53.580932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:59.578093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:05.558716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:12.388739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:18.536754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:24.836407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:31.373142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:37.513577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:44.360439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:50.899344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:55.582125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:02.222190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:09.308158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:16.443132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:22.518150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:28.978448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:35.117545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:41.334320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:47.585458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:53.914143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:59.944036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:05.891103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:12.788524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:18.906542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:25.229127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:31.716239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:37.815940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:44.683085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:51.191228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:56.017114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:02.791795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:09.692231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:16.744388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:22.874733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:29.253403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:35.414495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:41.728139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:47.901809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:54.248488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:00.294592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:06.292848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:13.086160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:19.189437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:25.655477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:31.999429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:38.704420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:45.095491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:51.565017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:56.340565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:03.097777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:10.140661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:16.992753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:23.224731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:29.569965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:35.718032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:42.081943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:48.185014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:54.529854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:00.579308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:06.609729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:13.356703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:19.452544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:26.012580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:32.354024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:39.020840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:45.386930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:51.962062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:56.644196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:03.512212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:10.450617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:17.243315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:23.556428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:29.848571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:36.007749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:42.484478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:48.854005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:54.828965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:00.895100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:06.859606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:13.606146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:19.704928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:26.359653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:32.688573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:39.330144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:45.762186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:52.258080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:56.934657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:03.765196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:10.761201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:17.526546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:23.840798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:30.088014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:36.295816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:42.834001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:49.138599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:55.157681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:01.259048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:07.174508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:13.971109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:19.936985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:26.686846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:32.952147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:39.596847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:46.209291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:52.577756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:57.216990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:04.080959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:11.146854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:17.806335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:24.097284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:30.735759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:36.603681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:43.182763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:49.434104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:55.478126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:01.564051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:07.491074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:14.273772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:20.320794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:26.977862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:33.207528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:39.884835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:46.528262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:52.960497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:57.647674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:04.504316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:11.444314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:18.198148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:24.462924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:31.005662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:36.936981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:43.550251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:49.753853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:55.827195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:01.894258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:07.876135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:14.588435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:20.653003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:27.318545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:33.607505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:40.247467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:46.760639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:53.342334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:58.056374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:04.864401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:11.861003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:18.526095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:24.787577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:31.435795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:37.193034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:43.833027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:50.042781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:56.162594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:02.133380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:08.157614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:14.856642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:20.970971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:27.655190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:33.922547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:40.550581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:47.037704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:53.694391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:58.399839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:05.219605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:12.224627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:18.859070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:25.123878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:31.738046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:37.569190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:44.235886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:50.420249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:56.462645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:02.391441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:08.476582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:15.188114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:21.321644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:27.991433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:34.306401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:41.004891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:47.309585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:53.950107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:58.797344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:05.565451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:12.610678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:19.144277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:25.481716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:32.053000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:37.903421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:44.573205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:50.721813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:56.811708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:02.827106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:08.789768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:15.504601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:21.604509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:28.250060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:34.606834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:41.431627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:47.710366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:54.228757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:59.176002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:05.929170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:13.018401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:19.460111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:25.828870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:32.383432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:38.283451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:44.883843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:51.014498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:57.073044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:03.110845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:09.124342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:15.786999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:21.870134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:28.542892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:34.890653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:41.804783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:47.998355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:54.492616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:34:59.499516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:06.273704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:13.467752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:19.759182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:26.180816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:32.610902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:38.600987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:45.184732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:51.268065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:35:57.362798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:03.392943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:09.395653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:16.072261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:22.169898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:28.823413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:35.305756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:42.082198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-01T23:36:48.318164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-07-01T23:37:15.286613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-01T23:37:16.154203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-01T23:37:16.870816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-01T23:37:17.374296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-01T23:37:17.659497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-01T23:36:55.134270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-01T23:36:58.601222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexAñoposMesgrupoclasesubgrupoLineaRef.Descripcionq ini$ iniQ pro$proQ com$ comQ ven$ venQ dev$ devQ tra$ traQ s/e$ s/eq fin$ finQ ven2$ ven3fecha
0827201911120LONAS360 FLIP MENDiscoveryAF20050360 FLIP ROJO CROYDON8.0194728.00.00.00.00.01.024341.00.00.00.00.00.00.07.0170387.01.024341.02019-01-01
1828201911120LONAS360 FLIP MENDiscoveryAF20080360 FLIP VERDE CROYDON12.0292092.00.00.00.00.01.024341.00.00.00.00.00.00.011.0267751.01.024341.02019-01-01
2829201911120LONAS50-50 MENDiscoveryAD9001950-50 GRIS OSCURO CROYDON13.0360581.00.00.00.00.01.027737.00.00.00.00.00.00.012.0332844.01.027737.02019-01-01
3830201911120LONAS50-50 MENDiscoveryAD9002050-50 GRIS CROYDON8.0221896.00.00.00.00.02.055474.00.00.00.00.00.00.06.0166422.02.055474.02019-01-01
4831201911120LONASABDEL BOYDiscoveryAE55020ABDEL GRIS CROYDON29.01079090.00.00.00.00.03.0111630.01.037210.00.00.00.00.027.01004670.02.074420.02019-01-01
5832201911120LONASABDEL BOYDiscoveryAE55040ABDEL CAFE CROYDON11.0409310.00.00.00.00.01.037210.00.00.00.00.00.00.010.0372100.01.037210.02019-01-01
6833201911120LONASABDEL MENDiscoveryAE55020ABDEL GRIS CROYDON30.01185720.00.00.00.00.04.0158096.01.039524.00.00.00.00.027.01067148.03.0118572.02019-01-01
7834201911120LONASABDEL MENDiscoveryAE55040ABDEL CAFE CROYDON8.0316192.00.00.00.00.02.079048.01.039524.00.00.00.00.07.0276668.01.039524.02019-01-01
8835201911120LONASABIE BOYDiscoveryAK65020ABIE GRIS CROYDON11.0254826.00.00.00.00.00.00.00.00.00.00.00.00.011.0254826.00.00.02019-01-01
9836201911120LONASABIE BOYDiscoveryAK65060ABIE AZUL CROYDON198.04586868.00.00.00.00.098.02270268.08.0185328.00.00.0-38.0-880308.070.01621620.090.02084940.02019-01-01

Last rows

df_indexAñoposMesgrupoclasesubgrupoLineaRef.Descripcionq ini$ iniQ pro$proQ com$ comQ ven$ venQ dev$ devQ tra$ traQ s/e$ s/eq fin$ finQ ven2$ ven3fecha
85811398092202155703PEGADO EN FRIOLORIK NEGRO JUNColeg. Croydon PvcAJ16090LORIK NEGRO CROYDON487.06794624.00.00.00.00.02.027904.00.00.00.00.0-1.0-13952.0484.06752768.02.027904.02021-05-01
85812398093202155703PEGADO EN FRIOLORIK NEGRO MENColeg. Croydon PvcAJ16090LORIK NEGRO CROYDON424.06700048.00.00.00.00.01.015802.00.00.00.00.00.00.0423.06684246.01.015802.02021-05-01
85813398094202155703PEGADO EN FRIOLORIK NGRO/NGRO BOYColeg. Croydon PvcAJ16019COLEGIAL LORIK NEGRO NEGRO2751.044032158.00.00.00.00.01.016086.00.00.00.00.00.00.02750.044016072.01.016086.02021-05-01
85814398096202155703PEGADO EN FRIOLORIK NGRO/NGRO JUNColeg. Croydon PvcAJ16019COLEGIAL LORIK NEGRO NEGRO2825.041366439.00.00.00.00.05.073888.04.060371.00.00.00.00.02824.041352922.01.013517.02021-05-01
85815398098202155703PEGADO EN FRIOSLASH AZUL BOYColeg. Croydon PvcCG29060SLASH NEGRO CROYDON2767.045993074.00.00.00.00.00.00.00.00.00.00.00.00.02767.045993074.00.00.02021-05-01
85816398099202155703PEGADO EN FRIOSLASH AZUL MENColeg. Croydon PvcCG29060SLASH NEGRO CROYDON1229.021899551.00.00.00.00.00.00.00.00.00.00.00.00.01229.021899551.00.00.02021-05-01
85817398100202155703PEGADO EN FRIOSLASH BLANCO BOYColeg. Croydon PvcCG29010SLASH BLANCO BLANCO CROYDON21777.0361977294.00.00.00.00.0311.05169442.0116.01928152.00.00.0-1.0-16622.021581.0358719382.0195.03241290.02021-05-01
85818398101202155703PEGADO EN FRIOSLASH BLANCO MENColeg. Croydon PvcCG29010SLASH BLANCO BLANCO CROYDON12259.0218443121.00.00.00.00.0221.03937999.072.01282968.00.00.0-1.0-17819.012109.0215770271.0149.02655031.02021-05-01
85819398102202155703PEGADO EN FRIOSLASH NEGRO BOYColeg. Croydon PvcCG29090SLASH NEGRO CROYDON7040.0117018880.00.00.00.00.035.0581770.09.0149598.00.00.00.00.07014.0116586708.026.0432172.02021-05-01
85820398103202155703PEGADO EN FRIOSLASH NEGRO MENColeg. Croydon PvcCG29090SLASH NEGRO CROYDON3127.055720013.00.00.00.00.027.0481113.04.071276.00.00.01.017819.03105.055327995.023.0409837.02021-05-01